Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time. The advent of high-spatial-resolution remote-sensing imagery further provides opportunities to apply change detection with object-based image analysis (OBIA), that is, object-based change detection (OBCD). When compared with the traditional pixel-based change paradigm, OBCD has the ability to improve the identification of changes for the geographic entities found over a given landscape. In this article, we present an overview of the main issues in change detection, followed by the motivations for using OBCD as compared to pixel-based approaches. We also discuss the challenges caused by the use of objects in change detection and provide a conceptual overview of solutions, which are followed by a detailed review of current OBCD algorithms. In particular, OBCD offers unique approaches and methods for exploiting high-spatial-resolution imagery, to capture meaningful detailed change information in a systematic and repeatable manner, corresponding to a wide range of information needs.

Over the last two decades (since ca. 2000), Geographic Object-Based Image Analysis (GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote-sensing imagery. During this time, research interests have demonstrated a shift from the development of GEOBIA theoretical foundations to advanced geo-object-based models and their implementation in a wide variety of real-world applications. We suggest that such a rapid GEOBIA evolution warrants the need for a systematic review that defines the recent developments in this field. Therefore, the main objective of this paper is to elucidate the emerging trends in GEOBIA and discuss potential opportunities for future development. The emerging trends were found in multiple subfields of GEOBIA, including data sources, image segmentation, object-based feature extraction, and geo-object-based modeling frameworks. It is our view that understanding the state-of-the-art in GEOBIA will further facilitate and support the study of geographic entities and phenomena at multiple scales with effective incorporation of semantics, informing high-quality project design, and improving geo-object-based model performance and results.

Forests are an important global resource, playing key roles in both the environment and the economy. The implementation of quality national monitoring programs is required for the generation of robust national statistics, which in turn support global reporting. Conventional monitoring initiatives based on samples of field plots have proven robust but are difficult and costly to implement and maintain, especially for large jurisdictions or where access is difficult. To address this problem, air photo- and satellite-based large area mapping and monitoring programs have been developed; however, these programs also require ground measurements for calibration and validation. To mitigate this need for ground plot data we propose the collection and integration of light detection and ranging (lidar) based plot data. Lidar enables accurate measures of vertical forest structure, including canopy height, volume, and biomass. Rather than acquiring wall-to-wall lidar coverage, we propose the acquisition of a sample of... Les forêts constituent une ressource importante à l’échelle du globe, celles-ci jouant un rôle essentiel tant au niveau de l'environnement que de l’économie. La mise en place de programmes de suivi de qualité à l’échelle nationale est essentielle pour la production de statistiques nationales robustes qui en retour soutiennent les activités de diffusion des données (global reporting) à l’échelle du globe. Les initiatives conventionnelles de suivi basées sur des échantillons de placettes se sont avérées robustes, mais difficiles et coûteuses à mettre en place et à tenir à jour, spécialement pour les grandes unités administratives ou là où l'accès est difficile. Pour solutionner ce problème, on a développé des programmes de cartographie et de suivi à grande échelle basés sur l'utilisation des photographies aériennes et des données satellitaires; toutefois, ces programmes nécessitent aussi des mesures de terrain pour les besoins d’étalonnage et de validation. Pour répondre à ce besoin pour des données de plac...

Remote sensing has been widely adopted to map post-fire burn severity over large forested areas. Statistical regression based on linear or simple non-linear assumptions is typically used to link post-fire forest reflectance with the degree of burn severity. However, this linkage becomes complicated if forests experienced severe mortality caused by pre-fire disease or insect outbreaks, which is likely to occur more frequently as a result of rapid climate change. In an effort to improve the understanding of the relationship between forest reflectance and fire-disease disturbances, this study explored the efficacy of three machine learning techniques, that is, Gaussian process regression (GPR), random forests (RF) and support vector regression (SVR), within a geographic object-based image analysis (GEOBIA) framework to assess burn severity in a forest subject to pre-fire disease epidemics. MASTER [MODIS (Moderate Resolution Imaging Spectroradiometer)/ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)] airborne sensor was applied to collect relatively high spatial (4 m) and high spectral (50 bands) resolution reflectance data. Results show that RF, SVR and GPR models outperformed conventional multiple regression by 48%, 29% and 27%, respectively. Compared to SVR and GPR, RF not only achieved better performance in burn severity assessment, but also demonstrated lower sensitivity to the application of different combinations of remote sensing variables. In addition to Normalized Burn Ratio (NBR), this study further revealed the importance of image-texture (representing spectral variability within and between neighbourhood forest patches) in burn severity mapping over diseased forests.

Near-surface air temperature (T_a) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum T_a (T_max⁡ and T_min⁡) is particularly valuable and critically needed in the scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily T_max⁡ and T_min⁡ at 1 km resolution over land across 50^∘ S–79^∘ N from 2003 to 2020 through the combined use of ground-station-based T_a measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates ranged from 1.20 to 2.44 ^∘C for T_max⁡ and 1.69 to 2.39 ^∘C for T_min⁡. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship between T_a and elevation and a positive relationship between T_a and land surface temperature; it captured spatial and temporal patterns of T_a realistically. This global 1 km gridded daily T_max⁡ and T_min⁡ dataset is the first of its kind, and we expect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data have been published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).